@RyanNg1403
Basic information
A project that integrates mcp servers for Prover9/Mace4 for a logical reasoning agent
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-server-logical-solver": {
"command": "python",
"args": [
"test.py"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
MCP Server Logical Solver is a logical reasoning system that combines Large Language Models (LLMs) with formal theorem proving through Prover9/Mace4. It processes logical problems in natural language and First‑Order Logic (FOL) to provide automated reasoning and structured explanations.
Clone the repository, install dependencies with pip install -r requirements.txt, configure a .env file with your model provider and API key, and set up mcp_config.json to point to the MCP‑Logic server. Run a single problem with python test.py or batch process via python main.py input.json output.json.
When Prover9 cannot find a proof, the system uses LLM‑based analysis to distinguish between a “False” result (premises contradict the conclusion) and an “Uncertain” result (insufficient premises).
Inputs must follow Prover9 syntax: use `all` for ∀, `exists` for ∃, `&` for ∧, `|` for ∨, `->` for →, `<->` for ↔, and `-` for ¬. XOR (⊕) is automatically transformed to an equivalent form.
You must have the MCP‑Logic server installed and running, Python 3, the packages in `requirements.txt`, and an API key for the chosen LLM provider (unless using Ollama).
The system returns a JSON object containing original premises/conclusions (natural language and FOL), the answer (`True`/`False`/`Uncertain`), a detailed explanation, and a `tool_use` boolean indicating whether tool calling succeeded.
Yes, by setting the model provider to `ollama` in the `.env` file and running a local model; no API key is required for Ollama.
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